The present invention relates to multi-slice helical computerized tomography and more particularly to an algorithm, method and apparatus for using the same which reduces the data acquisition time and data processing time required to generate an image.
In computerized tomography (CT) X-ray photon rays are directed through a region of interest (ROI) in a patient toward a detector. Attenuated rays are detected by the detector, the amount of attenuation indicative of the make up (e.g. bone, flesh, air pocket, etc.) of the ROI through which the rays traversed. The attenuation data is then processed and back-projected according to a reconstruction algorithm to generate an image of the ROI's internal anatomy. Generally, the “back projection” is performed in software but, as the name implies, is akin to physically projecting rays from many different angles within an image plane through the image plane, the values of rays passing through the same image voxels being combined in some manner to have a combined effect on the voxel in the resulting image. Hereinafter the data corresponding to rays which are back projected will be referred to as back projection rays.
During data acquisition, if a patient moves, artifacts can occur in the resulting image which often render images useless or difficult to use for diagnostics purposes. For this and other reasons, as in other imaging techniques, the CT industry is constantly trying to identify ways to reduce the duration of acquisition periods without reducing the quality of acquired data.
In addition, because huge amounts of data are acquired during an acquisition period and the processing methods for image reconstruction from the gathered data are relatively complex, a huge number of calculations are required to process data and reconstruct an image. Because of the huge number of required calculations, the time required to process collected data and reconstruct an image is appreciable. For this reason the CT industry is also constantly searching for new processing methods and algorithms which can speed up the reconstruction process.
Various CT system features and procedures have been developed to increase data acquisition speed and to speed up the reconstruction process. Some of the more popular features and procedures including fan beam acquisition, simultaneous multiple slice acquisition, helical scanning and half-scanning. In fan beam acquisition the source is collimated into a thin fan beam which is directed at a detector on a side opposite a patient. In this manner, a complete fan beam projection data set is instantaneously generated for a beam angle defined by a central ray of the source fan beam. The source and detector are rotated about an image plane to collect data from all (e.g., typically 360 degrees) beam angles. Thereafter the collected data is used to reconstruct an image in the image plane. Thus, fan beam acquisition reduces acquisition period duration.
With respect to half-scanning, assuming a patient remains still during a data acquisition period, conjugate data acquisitions (i.e., data acquired along the same path from opposite directions) should be identical. In addition, using a fan beam, at least one ray can be directed through an image plane from every possible beam angle without having to perform a complete rotation about the patient. As known in the industry, data corresponding to every beam angle corresponding to a single imaging plane can be collected after a (π+2γ)/2π rotation about the patient. Because less than an entire rotation about the imaging plane is required to acquire the imaging data these acquisition methods and systems are generally referred to as half-scan methods and systems. Thus, half-scan acquisition has been employed to reduce acquisition period duration in conjunction with single row detectors. In addition, because relatively less data has to be processed in the case of half-scan imaging methods and systems to generate an image, half-scan methods and systems also have the advantage of potentially reducing data processing and reconstruction times.
While fan beams and half-scans have several advantages, often, during a diagnostics session, a system operator will not know the precise location within a patient of an object, cavity, etc., of interest to be imaged. For this reason, it is advantageous for a system operator to be able to generate several cross sectional images in rapid succession by selecting different image/reconstruction planes. In these cases rapid data processing is extremely important to minimize delays between image generation so that a user does not lose her train of thought between image views.
Single slice detectors, fan beams and half-scans can be used to generate data in several different parallel image planes which, after data acquisition, can be used by a processor to generate an image anywhere between the image planes through interpolation/extrapolation procedures known in the art. While such systems work, unfortunately, the acquisition time required to generate data corresponding to many image planes is excessive and inevitable patient movement often causes image artifacts.
One way to speed up data acquisition corresponding to several image planes is by employing a multi-row detector with a fan beam. In multi-row detector systems, a relatively thick fan beam is collimated and directed at a multi-row detector with a patient there between, each detector row in effect gathering data for a separate “slice” of the thick fan beam along the Z or translation axis perpendicular to a fan beam width. Despite each detector row having a thickness, in these systems it is assumed that the detected signals in each row correspond to a plane centered within the row as projected onto the isocenter Z. Hereinafter the central plane through a row will be referred to as a row center.
After data acquisition an interface enables a system user to select an image plane from within the area corresponding to the collected data. The selected image plane is typically between the row centers of at least two adjacent detector rows. After image plane selection, a processor interpolates between data corresponding to adjacent rows to generate back projection rays corresponding to the selected image plane. When another image corresponding to a different image plane is desired, after selecting the plane, the processor again identifies an acquired data subset for interpolation, additional processing and back projection. Thus, multi-row detector systems further reduce data acquisition period duration where several image planes may be selected for reconstruction.
One limitation with multi-row detectors is that, during a single acquisition period, data can only be collected which corresponds to the detector thickness. To collect additional data corresponding to a ROI that is linger in the Z axis than the width of the detector, after one acquisition period corresponding to a first portion of the ROI, the patient has to be moved along the Z axis until a second portion of the ROI which is adjacent the first portion of the ROI is between the source and detector. Thereafter a second acquisition process has to be performed. Similarly, to collect additional data corresponding to a third portion of the ROI the patient has to be transported to another relative location with respect to the source and detector. Required translation without acquisition necessarily prolong the acquisition period and the additional acquisition time and aligning processes inevitably result in relative discomfort, additional patient movements and undesirable image artifacts.
Helical scanning systems have been developed so that data can be collected during a single acquisition period without halting patient translation during the acquisition period. In a helical scanning system, the source and detector array are mounted on opposing surfaces of an annular gantry and are rotated there around as a patient is transported at constant speed through the gantry. The X-ray beam sweeps a helical path through the ROI, hence the nomenclature “helical scanning system”. Data acquisition can be sped up by increasing operating pitch (i.e., table translation speed relative to gantry rotation rate).
Various combinations of the fan-beam, multi-slice and half scan and helical scanning features have been combined to realize synergies and have been somewhat successful. By combining various of the speed enhancing features, data acquisition period durations are appreciably reduced thereby increasing system throughput and increasing image quality by minimizing the likelihood of patient movement.
While systems that combine several speed enhancing features are fast becoming a standard, not surprisingly, because of the complex data acquisition cycles that take place in such systems, the algorithms needed to combine subsets of acquired data into images have become extremely complex. To this end, exemplary helical weighting algorithms are described in an article entitled “Multi-Slice Helical CT: Scan and Reconstruction” by Hui Hu which was published in the January 1999 issue of Medical Physics, vol. 26, No. 1, pages 1 through 14. In operation, after imaging data has been collected and archived for a specific three dimensional ROI, an imaging system operator selects a specific slice and slice thickness through the ROI for image reconstruction and display. When a slice is selected, the processor applies a weighting and filtering function to the data to generate the intended image. The weighting function is dependent upon which slice is selected for reconstruction and viewing and on the pitch at which the data was collected. Therefore, each time a new slice is selected, a completely different weighting function which is pitch and slice dependent, has to be determined and applied to the acquired data and the weighted projection data has to be re-filtered again to generate a desired image.
While helical weighting function may take any of several different forms, an exemplary helical weighting function corresponding to a single slice image at a Z axis location Z1 typically includes a triangle having a value of one at a triangle apex Z1 location and tapering off to either side thereof down to zero.
Complicating matters, according to some diagnostic techniques it is advantageous to generate a two dimensional image corresponding to a “thick slice” through a ROI or a thick slice of interest (TSOI). For instance, in some cases it may be advantageous to generate a two dimensional image of a 10 mm thick volume through a ROI. Algorithms to generate a “thick image” have to combine data from several different two dimensional slice images through the TSOI. Early algorithms that used helical data to generate a thick image first generated a plurality of separate thin slice images through the TSOI using standard weighting, combining, filtering and back projection techniques and then combined the separate slice images using one of several different “Z-smoothing” functions that weight data from each image generally as a function of distance from a central imaging plane that bisected the TSOI.
In addition to being extremely slow, these early techniques were cumbersome and difficult to use as several imaging parameters had to be selected by a system operator that affected final image quality and often the only way to determine if optimal parameters had been selected was to generate and observe various characteristics of resulting images. For instance, some important parameters include image artifacts, noise, the well known slice sensitivity profile (SSP: i.e., response of the detector to a small homogeneous object as a function of the object position along the Z axis) and related full width at half maximum measurement (FWHM: i.e., full width of the SSP at half the maximum amplitude of the SSP), etc. Where the resulting image characteristics were unacceptable (e.g., excessive noise occurred, etc.), the operator had to alter the imaging parameters, regenerate images corresponding to the new parameters and then observe the images to determine if acceptable characteristics resulted.
One of the most important thick image imaging parameters is the number N of slice images (i.e., TSOI slice images) through the TSOI that are combine to generate the thick image. Ideally the number N should be as small as possible so that the processing time required to generate the thick image is minimized. However, if the number of slice images combined to generate a thick image is to small, the shape of the SSP becomes irregular making it very difficult to measure slice thickness or the FWHM. In addition, if the number of combined images is to small the resulting image may have some irregularities and may not actually provide a true representation of the anatomical structures within the TSOI.
Two other well known system parameters that are important in the case of thick image generation include a Z-smoothing factor zsf and an image weighting factor kw that are typically set during a commissioning process by iterative adjustment and as a function of resulting image characteristics. Several optimization schemes have been developed to optimize factors zsf and kw and any of those schemes may be employed in conjunction with the present invention. See for instance, U.S. Pat. No. 6,295,331 entitled “Methods And Apparatus For Noise Compensation In Imaging Systems” which issued on Sep. 25, 2001 and U.S. Pat. No. 6,173,029 entitled “Higher Order Detector Z-Slope Correction For A Multislice Computed Tomography System” which issued on Jan. 9, 2001, and which are assigned to the assignee of the present invention.
Recently algorithms have been developed that skip the intermediate processes associated with generating separate slice images through a TSOI and instead, after a TSOI has been selected by an operator, weight and combine helical data to directly generate a thick image corresponding to the TSOI. To this end, after factors zsf and kw have been optimally set, a TSOI has been selected and the number N of slice images through the TSOI to be combined has been selected, a processor identifies Z-smoothing weights for each slice image and combines the Z-smoothing weights and helical weights for each image to generate a thick image weighting function corresponding to an entire helical data sub-set that is subsequently used to generate the thick image.
Because a typical slice image helical weighting function extends along the Z-axis and often the number of TSOI slice images to be combine to generate a thick image requires spatially close slice images, often the helical weighting functions corresponding to adjacent TSOI slice images overlap. Overlapping weighting functions along with hardware constraints have caused, prior processes of combining helical slice weighting functions to be relatively computationally complex requiring various reading, adding and writing steps.
To this end, exemplary hardware employed to apply z-smoothing weights and combine overlapping helical weighting functions to generate a thick image weighting function includes a processor having first and second blocks of internal memory where first and second functions to be added together have to be stored in the separate first and second blocks, respectively.
With the limited hardware architecture described above, assume that each two adjacent weighting functions corresponding to four adjacent slice images overlap along a range of gantry angles. In this case, prior algorithms to apply z-smoothing weights to each of the first and second functions and then add the functions together included applying the z-smoothing weight to the first helical weighting function and storing the resultant in the second block and then applying the z-smoothing weight to the second helical weighting function and storing the resultant in the first block. Thereafter, to add the first two helical weighting functions together included, for each gantry angle β corresponding to either the first or second helical weighting function, reading a first weight corresponding to the first weighting function from the second block, reading a second weight corresponding to the second weighting function from the first block, adding the first and second weights together and then writing the summed weight to a final weighting array. The final array is stored within the first block. This four step process had to be repeated for every gantry angle β corresponding to at least one of the first and second weighting functions.
Thereafter, to apply a z-smoothing weight to the third helical weighting function and add the resultant to the final (i.e., summed) weighting function a z-smoothing weight corresponding to the third image was applied to the function in the first block and the resultant was restored in the first block. Thereafter, for each gantry angle corresponding to either the original second weighting function or the third weighting function, the algorithm included reading a first weight corresponding to the final weighting function, reading a second weight corresponding to the third weighting function from the first block, adding the first and second weights together and then writing the summed weight to a final weighting array. Once again. this four step process had to be repeated for every gantry angle corresponding to at least one of the second and third weighting functions. A similar process had to be performed to apply z-smoothing weight to the fourth helical weighting function and add the resultant to the final weighting function.
While four process cycles may not, at first blush, appear to be extremely burdensome, when there are a massive number of such combinations, the combined periods have been known to cause delays as long as several seconds between thick slice selection and image presentation. While several seconds may not seem extremely time consuming where only a single thick slice image is required, often system operators prefer to select different thick images for display in rapid succession so that various portions of the ROI can be observed during an imaging session. In these cases even a few seconds can seem like an eternity to a system operator attempting to quickly cycle through images to identify a specific anatomical phenomenon.
Thus, it would be advantageous to have a system that could help a system operator optimize various system parameters associated with thick image processing thereby reducing the number of iterations required to generate an acceptable image and reducing uncertainty regarding whether or not a final image is the best possible image and that could speed up the processing time required to generate a thick image after a TSOI has been selected.